A large-scale AI-assisted QA/QC analysis of microplastic data across human exposure pathways

Reliable exposure data is critical for determining the potential human health risks of microplastics. To date, however, exposure data are often difficult to compare and do not always meet quality criteria, limiting their suitability for risk assessment. This study aimed to provide a large-scale quality assessment (QA/QC) of studies on microplastics in the main human exposure routes: Air, Food, and Beverages. A hybrid method was used, in which an AI model (Gemini 1.5-pro) automatically extracted information from 950 studies, followed by logical score assignment using Python scripts. Building on earlier QA/QC screenings, each study was evaluated against 13 criteria and assigned a score of 2 (fully reliable), 1 (reliable with restrictions), or 0 (unreliable). AI performance was verified by human assessment in a statistically determined sample of studies ( n = 87). The average total score across all datasets was 12 out of 26, with an average of 5.3 inadequate scores (zero scores) per study. The highest-scoring studies achieved 20 points. The lowest scores were associated with criteria involving positive and negative controls, blank correction, and the representativeness of sampling and usage conditions. The results show that most current studies have significant shortcomings for application in risk assessment. In particular, data on Food and Air may not adequately represent actual human exposure. At the same time, this QA/QC analysis is the largest to date and the first to cover Air, Food, and Beverages simultaneously; it provides a valuable ranking of usable studies and makes large-scale risk assessment more feasible. The dataset can assist policymakers in selecting the most reliable data for health evaluations and underscores the need for standardization and improved analytical methods in future studies.